Evidence map›Paper›PMID 29285840›Full record

ReviewPharmacoepidemiology and drug safety2018

Methods for time-varying exposure related problems in pharmacoepidemiology: An overview.

Laura Pazzagli, Marie Linder, Mingliang Zhang, Emese Vago, Paul Stang, David Myers, Morten Andersen, Shahram Bahmanyar

Erratum issuedAbstract readReview
In one paragraph

Review in Pharmacoepidemiology and drug safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 77 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021
    Pooled it
  3. Efficacy and safety of tocilizumab in COVID-19 patients: a living systematic review and meta-analysis.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021
    Pooled it
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  12. A scoping review of real-world evidence generation methods to estimate the safety of type 2 diabetes medications on fracture risk.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Laura PazzagliCentre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-1908-6073
Marie LinderCentre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Mingliang ZhangJanssen, Beerse, Belgium.
Emese VagoJanssen, Beerse, Belgium.
Paul StangJanssen, Beerse, Belgium.
David MyersJanssen, Beerse, Belgium.
Morten AndersenCentre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Shahram BahmanyarCentre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLack of control for time-varying exposures can lead to substantial bias in estimates of treatment effects. The aim of this study is to provide an overview and guidance on some of the available methodologies used to address problems related to time-varying exposure and confounding in pharmacoepidemiology and other observational studies. The methods are explored from a conceptual rather than an analytical perspective.

methodsThe methods described in this study have been identified exploring the literature concerning to the time-varying exposure concept and basing the search on four fundamental pharmacoepidemiological problems, construction of treatment episodes, time-varying confounders, cumulative exposure and latency, and treatment switching.

resultsA correct treatment episodes construction is fundamental to avoid bias in treatment effect estimates. Several methods exist to address time-varying covariates, but the complexity of the most advanced approaches-eg, marginal structural models or structural nested failure time models-and the lack of user-friendly statistical packages have prevented broader adoption of these methods. Consequently, simpler methods are most commonly used, including, for example, methods without any adjustment strategy and models with time-varying covariates. The magnitude of exposure needs to be considered and properly modelled.

conclusionsFurther research on the application and implementation of the most complex methods is needed. Because different methods can lead to substantial differences in the treatment effect estimates, the application of several methods and comparison of the results is recommended. Treatment episodes estimation and exposure quantification are key parts in the estimation of treatment effects or associations of interest.

Indexed as

BiasData Interpretation, StatisticalDrug-Related Side Effects and Adverse ReactionsHumansObservational Studies as TopicPharmacoepidemiologyPractice Guidelines as TopicStudy Guides as TopicTime FactorsTreatment Outcomecumulative exposure and latencypharmacoepidemiologytime-varying confounderstime-varying exposuretreatment episodestreatment switching

Identifiers

PMID29285840
PMCPMC5814826

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.